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2019

Impact of the Prophecy Job Fit Predictor on NewGraduate Nurse SatisfactionJoi Alesha JohnsonWalden University

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Walden University

College of Health Sciences

This is to certify that the doctoral study by

Joi Johnson

has been found to be complete and satisfactory in all respects,

and that any and all revisions required by

the review committee have been made.

Review Committee

Dr. Courtney Nyange, Committee Chairperson, Nursing Faculty

Dr. Susan Hayden, Committee Member, Nursing Faculty

Dr. Linda Matheson, University Reviewer, Nursing Faculty

Chief Academic Officer

Eric Riedel, Ph.D.

Walden University

2019

Abstract

Impact of the Prophecy Job Fit Predictor on New Graduate Nurse Satisfaction

by

Joi Johnson

MSN, Walden University, 2010

BSN, Barry University, 2004

Project Submitted in Partial Fulfillment

of the Requirements for the Degree of

Doctor of Nursing Practice

Walden University

July 2019

Abstract

Research has shown that job satisfaction influences retention of nurses, and policies

focused on nursing satisfaction are more beneficial for retaining new nurses than

adjusting work hours and wages. The prophecy job fit predictor is a quality improvement

initiative designed to identify where a nurse should be assigned based on behavior,

clinical capabilities, and personality assessment. The practice-focused question for this

project focused on whether satisfaction rates of recently graduated registered nurses were

influenced by their unit placement. The conceptual frameworks that guided this project

were the plan, do, study, and act method and Herzberg’s 2-factor theory. Data were

obtained from surveying a cohort of 54 graduate nurses in 3 hospital locations in 6

specialty units. Results obtained using 1-way ANOVA and a Likert scale showed that

graduate nurse satisfaction rates increased when assigned to their best fit unit: prophecy

job fit 58.33% with a mean score of 3.34 (Hospital A), prophecy job fit 20% with a mean

score of 3.1 (Hospital B), prophecy job fit 33.33% with a mean score of 3.1 (Hospital C).

The results showed that the prophecy job fit predictor during nursing orientation can

guide nurses into the appropriate specialty unit and increase nursing satisfaction. The

implications of these findings for positive social change in nursing practice include the

benefits of using the prophecy job fit predictor when assigning graduate nurses to their

hospital setting to address the nursing shortage.

Impact of the Prophecy Job Fit Predictor on New Graduate Nurse Satisfaction

by

Joi Johnson

MSN, Walden University, 2010

BSN, Barry University, 2004

Project Submitted in Partial Fulfillment

of the Requirements for the Degree of

Doctor of Nursing Practice

Walden University

July 2019

Dedication

I have dedicated this DNP project to my twin children, Brandon and Eden, who

were my constant motivation and joy. To my family and friends who supported me

during this journey and who always believed in me. Thank you for helping me make my

dreams come true.

i

Table of Contents

List of Tables ..................................................................................................................... iii

List of Figures .................................................................................................................... iv

Section 1: Nature of the Project ...........................................................................................1

Introduction ....................................................................................................................1

Problem Statement .........................................................................................................1

Purpose Statement ..........................................................................................................3

Nature of Doctoral Project .............................................................................................4

Significance....................................................................................................................6

Summary ........................................................................................................................7

Section 2: Background and Context ....................................................................................9

Introduction ....................................................................................................................9

Concepts, Models, and Theories ....................................................................................9

Definition of Terms......................................................................................................10

Relevance to Nursing Practice .....................................................................................11

Local Background and Context ...................................................................................12

Role of DNP Student ...................................................................................................14

Summary ......................................................................................................................16

Section 3: Collection and Analysis of Evidence ................................................................17

Introduction ..................................................................................................................17

Practice-Focused Question...........................................................................................17

Sources of Evidence .....................................................................................................18

ii

Archival and Operational Data ....................................................................................19

Analysis and Synthesis ................................................................................................20

Summary ......................................................................................................................21

Section 4: Findings and Recommendations .......................................................................22

Introduction ..................................................................................................................22

Findings and Implications ............................................................................................23

Data Analysis ........................................................................................................ 23

Recommendations ........................................................................................................30

Strengths and Limitations of the Project ......................................................................30

Section 5: Dissemination Plan ...........................................................................................32

Analysis of Self ............................................................................................................33

Summary ......................................................................................................................34

References ..........................................................................................................................36

iii

List of Tables

Table 1. Tests of Between-Subjects Effects ......................................................................28

Table 2. Tukey Post Hoc Test Among Means ...................................................................28

iv

List of Figures

Figure 1. Specialty unit satisfaction for Hospital A. ....................................................... 24

Figure 2. Overall unit satisfaction for Hospital A ........................................................... 24

Figure 3. Specialty unit satisfaction for Hospital B. ....................................................... 25

Figure 4. Overall unit satisfaction for Hospital B ........................................................... 25

Figure 5. Specialty unit satisfaction for Hospital C ........................................................ 26

Figure 6. Overall unit satisfaction for Hospital C ........................................................... 26

Figure 7. Bar graph illustrating the 80 percent and above best fit participants at each

hospital ...................................................................................................................... 27

Figure 8. Mean plot for three means among the hospitals. ............................................. 29

1

Section 1: Nature of the Project

Introduction

Despite preceptor programs and hospital led retention interventions, new graduate

nurses are unsatisfied and leaving the profession, leading to a high turnover rate among

this group. Though determining the actual retention rate for new graduate nurses in the

United States is difficult and current estimates vary (Blegen, Spector, Lynn, Barnsteiner,

& Ulrich, 2017), research has indicated that the transition for new graduate nurses into

the workforce is stressful due to heavy workloads and lack of professional nursing

competence (Labrague & McEnroe, 2018). Further, nurse turnover creates a shortage that

impacts patient care and causes nurse dissatisfaction, which may be caused by a lack of

job fit. New nurses may not be assigned to units that fit with their personality, skill set, or

preference (Lleixá et al., 2010). Therefore, this project was conducted to investigate job

fit and nursing satisfaction and obtain a greater understanding of new graduate nurse

turnover rates.

Problem Statement

The problem that I addressed in this project is the high turnover rates among new

graduate nurses by evaluating the impact of the Prophecy (2018) job fit predictor on

nursing satisfaction rates of recently graduated registered nurses in their hired hospital

setting. According to the National Council of State Boards of Nursing (2018),

approximately 25% of new nurses leave a position within their first year of practice.

Increased turnover negatively influences patient safety and health care outcomes, which

significantly impacts nursing practice (Eckerson, 2018). Additionally, the U.S. Bureau of

2

Labor Statistics (2018) projects that 1.1 million additional nurses will be needed to avoid

a continued shortage in health care. The nursing profession continues to face shortages

due to lack of potential educators, high turnover, and inequitable distribution of the

workforce (Sawaenqdee et al., 2016).

The new graduate nurse turnover rate has not been calculated at the project site

for this study; however, according to nurse recruiters at the project site, there has been a

consistent nursing shortage when new graduate nurses are started in the hospital,

especially when placed on specialty units. Additionally, best practices for graduate nurse

placement have not been researched, and no evidence-based practice guidelines have

been established. This gap-in-practice initiated the use of the Prophecy job fit predictor.

The data obtained from the Prophecy job fit predictor aids nurse recruiters and unit

managers when hiring new graduates, as it enables managers to make better-informed

decisions about nurse candidate placement (Prophecy, 2018). This project was conducted

to determine whether clinical unit placement contributes to new graduate nurses’ job

satisfaction and decreases turnover in a multihospital system, which can provide

managers and nurse recruiters guidelines for counseling new graduates after hiring and

provide direction when placing new graduate nurses in specialty units.

In today’s healthcare system, practitioners rely on evidence-based research to

ensure that best practices are being used in any health care setting (Leung, 2014).

However, hospitals across the country may or may not have guidelines in place for job

fit. It is important to create guidelines to transition new graduate nurses into the

profession, but this process does not always occur, causing difficulty for graduate nurses

3

adjusting to the new role (van Rooyen, Jordan, Ten Ham-Baloyi, & Caka, 2018). New

nurses may face challenges with performing clinical skills accurately, which affects the

quality of patient care (van Rooyen, 2018). Due to the uncertainty felt by many graduate

nurses, improvement measures should be implemented that guide job placement during

the initial phases of job entry are critical (Lleixá Fortuño, 2010).

Purpose Statement

Incremental steps have been taken toward achieving better quality of care for

patients in the United States; however, minimal advances have been initiated to ensure

new graduate nurses have optimal job placement (Ackerson, 2018). Evaluation of this

existing quality improvement initiative at the project site is due to uncertainty if the

Prophecy job fit predictor guides nurses into their best fit hospital unit and increases

nursing satisfaction resulting in nurse retention. Because no best practice guidelines have

been established for graduate nurse placement, the purpose of this project was to address

this gap-in-practice for new graduate nurses and specialty placement upon hiring. The

guiding practice-focused question for this doctoral project was: Are satisfaction rates of

recently graduated registered nurses influenced by their unit placement being based on

the Prophecy (2018) job fit predictor? To answer the practice-focused question, I

measured the satisfaction of new graduate nurses and analyzed data percentages from the

Prophecy job fit predictor to explain its impact.

Newly hired nurses experienced a positive influence on their future employment

choices when they receive administrative support during clinical placements and

assistance with future career planning (Boyd-Turner, Bell, & Russell, 2016). Nurses who

4

are on a unit with high acuity and responsibility feel comfortable if they are on their best

fit unit compared to those who are not on their best fit unit. Nurse leaders are held

accountable for staffing units at adequate levels to provide safe patient care (Fitzpatrick,

2017). When an organization achieves employee satisfaction, patient-centered can be

delivered across the continuum that provides seamless, affordable, and quality care (Polit,

2008). Personal characteristics and situational factors influence new graduate nurses’

satisfaction with the practice environment (Hussein, 2016). Completing the Prophecy job

fit predictor will allow new graduate nurses to be assigned to specialty units where their

clinical skills and temperament are compatible with the unit. Empowering new graduate

nurses by placing them in appropriate work environments may help increase nurse job

satisfaction, improve patient care quality, and reduce burnout (Kenny, Reeve, & Hall,

2016; Perry, 2018). This project may address the gap-in-practice of undeveloped unit

placement guidelines for new graduate nurses by analyzing the Prophecy job fit predictor

and determining if it impacts new graduate nurse satisfaction rates creating a best practice

for placement. The positive social change that may come about from this project is

establishing an evidence-based practice for placement of new graduate nurses being hired

in best fit units after graduation and eliminating this gap-in-practice.

Nature of Doctoral Project

I completed a literature review using the OVID, MEDLINE, and CINAHL

databases, and I engaged with clinicians (nurse managers, nurse educators, clinicians)

within the administration, education, and orientation programs to find the problem and

support the premise of the project. This best fit project was an evaluation of an existing

5

quality improvement initiative using the revised Casey-Fink graduate nurse experience

survey (Casey, Fink, Krugman, & Propst, 2004) and the Prophecy job fit predictor, which

is a quality improvement tool that measures behavioral attributes along with clinical

competencies and then determines appropriate job fit for new nurses(Prophecy, 2018).

The Casey-Fink graduate nurse experience survey consists of five focus areas:

demographic information, skills/procedure performance, comfort/confidence, job

satisfaction dimensions, and work environment and difficulties in role transition. This

survey takes 15 to 20 minutes to complete (Casey et al., 2004). Surveys are a cost

effective and easy way to collect data to evaluate if desired outcomes are being met

(Leung, Trevena, & Waters, 2014) and can provide insight on hospital issues. For

example, Murgia (2011) used a survey to assess nurse satisfaction and determined that

stress is a main contributor for increasing staff turnover and diminishing work

satisfaction that can result in reduced patient quality and quantity of care.

The hospital sites where this best fit project were conducted collects data from

graduate nursing students after a cohort has completed nursing orientation to include the

Prophecy job fit predictor and the Casey-Fink graduate nurse experience survey. The

results from the initial, 6-month, and 12-month Casey-Fink graduate nurse experience

surveys were used to assess nursing satisfaction. The hospital provided deidentified data

they collected during their quality improvement initiative for evaluation of the Prophecy

job fit predictor. Data show only unit placed and job fit percentage. Inferential analysis

was done to compare the mean Prophecy best fit predictor analysis scores of the cohort of

graduate nurse students placed in three hospitals to see if there were statistical

6

differences. These results were then analyzed to compare new graduate nurse satisfaction

rates and whether the new graduate nurses were placed in their best fit unit. Nurse

managers and other health care recruiters may apply the results of this project to their

hiring practices and improve the retention of new graduate nurses.

Significance

Steps have been taken toward achieving better quality of care for patients;

however, minimal advances have been initiated to ensure new graduate nurses have

optimal job placement (Ackerson, 2018). Contrary to placing new graduate nurses on

their best fit units, hospitals may place new graduate nurses according to hospital vacancy

and need (Lleixá Fortuño, 2010). The Prophecy job fit predictor calculates a best fit

percentage for new graduate nurses and determines the specialty units where their clinical

skills and temperament are best fit with the unit; therefore, determining whether the best

fit predictor correlates to nursing satisfaction can provide guidance for graduate nurse

placement policies. This analysis has implications for positive social change by

improving nursing satisfaction rates, which can decrease nursing shortages, reduce

turnover, and improve nurse retention on their assigned units.

This project has received organizational support by clinical system coordinators,

Prophecy leaders, nurse recruiters, managers, and staff. Having stakeholders support the

implementation of the project will improve use of the tool, provide a sense of direction,

and help accomplish the goals and objectives of the quality improvement initiative

(Kettner, 2017). Actions of support can also reduce or eliminate challenges by

encouraging use of the survey results and support of the project (Jiwan, 2018). The

7

stakeholders in this project include the chief nursing officer, directors of nursing units,

unit managers, charge nurses, and staff nurses. Chief nursing officers and directors could

reduce the cost of onboarding and orientation if nurses are placed in their best fit unit and

satisfied. The average cost for new graduate nurse orientation is $14,558 per nurse

(Reiter, 2008), so reducing the number of nurses who have to be oriented or reoriented

can impact organizational costs. If nurses are satisfied with the hospital unit where they

are hired, they will be less likely to quit or transfer, which would require replacement and

additional onboarding costs to the hospital.

The potential contributions of the best fit project are to share the analysis and

results with hospital chief executive officers, nurse managers, and nurse recruiters to

validate or challenge the current quality improvement practices. This project may provide

evidence-based data for the use of Prophecy job fit predictor when assigning new

graduate nurses to hospital units. Having statistical evidence from this DNP project can

contribute to local nursing practice and provide potential guidelines for transferability to

similar practice areas. Many hospitals use quality improvement tools to help stimulate

and support improvements in the quality of care delivered (Centers for Medicare and

Medicaid Services, 2017). Thus, transferability can be established hospitals in the area,

contributing to additional research efforts in improving nursing satisfaction rates and

decreasing turnover.

Summary

Many hospitals and clinics are moving toward quality improvement programs to

orient and evaluate new nursing staff (Eckerson, 2018). However, there is not a

8

standardized hiring process for new graduate nurses who will be assigned to specialty

units. In this DNP project, I addressed this gap-in-practice by evaluating an existing

quality improvement project specific to new graduate nurse job placement and nursing

satisfaction. Evaluating this quality improvement initiative will support creating a best

practice guideline for placement of new graduate nurses. When nurse leaders and

recruiters can place new graduate nurses in their best fit specialty this practice will allow

consistency in placing nurses and support retention of nurses. In Section 1, I explained

the rationale and purpose of this quality improvement project. In Section 2, I will review

conceptual frameworks and theories that I used as a basis for my analysis.

9

Section 2: Background and Context

Introduction

Graduate nurses are leaving the profession within their first year of practice,

contributing to high turnover rates and the national nursing shortage (National Council of

State Boards of Nursing, 2018). The practice-focused question for this doctoral project

was “Are nursing satisfaction rates of recently graduated registered nurses influenced by

their unit placement being based on the Prophecy job fit predictor?” The purpose of this

doctoral project was to evaluate graduate nurse satisfaction with their placement in

hospital units based on results from the Prophecy (2018) job fit predictor and the Casey-

Fink survey (Casey et al., 2004). In this section, I will address the concepts and models to

guide the project and discuss the best fit project’s relevance to nursing practice, local

background, and my role as a DNP student.

Concepts, Models, and Theories

The Institute for Healthcare Improvement (2016) developed a framework for

quality improvement projects called the plan, do, study, and act (PDSA) method. This

method is designed to supplement an organization’s existing change model and produce a

more efficient clinical practice, allowing organizations to continue to develop plans to

test the quality improvement change, analyze data, and then determine what

modifications need to be made (Institute for Healthcare Improvement, 2016). I used the

PDSA model to revise the quality improvement initiative as needed according to the

evaluation done in this DNP project. When using the PDSA with this project, I followed

the steps to make policy recommendations and updates according to the gathered results.

10

The plan and do phases have already been implemented, as reflected by the

organization’s implementation of the Prophecy job fit predictor. The study phase began

with the analysis of the data generated from this project. The act phase of this model will

be carried out by the organization following completion of this project and will be based

on the recommendations that are produced from the data analysis. The PDSA approach

was the most appropriate method to collect data and provide evidence of the impact of

the job fit predictor and nursing satisfaction because it helped determine whether the

change led to an improvement. The evaluation plan includes an impact evaluation to help

determine if the program has met its goal.

In addition to the PDSA approach, I used Herzberg’s two factor theory to justify

the need to address new nurse satisfaction and turnover rates. According to the theory,

certain characteristics of a job are consistently related to job satisfaction: achievement,

recognition, the work itself, responsibility, and advancement growth (Savoy & Wood,

2015). The three elements of Herzberg’s theory that relate to the Prophecy job fit

predictor and nursing satisfaction are achievement, the work itself, and responsibility. A

new graduate nurse who is not placed in their best fit unit will have issues with those

elements and, according to Herzberg’s theory, will not be satisfied, causing them to quit

or transfer, impacting nurse retention.

Definition of Terms

The following terminology and associated definitions are provided to enhance

reader familiarity with nursing terms unique to this project.

11

Casey-Fink graduate nurse survey: Surveys generated by Vizient and distributed

by the best fit project site that evaluates five focus areas: demographic information,

skills/procedure performance, comfort/confidence, job satisfaction dimensions, and work

environment and difficulties in role transition. These surveys contain information about

the satisfaction of the graduate nurses and were used to provide descriptive statistics.

Prophecy job fit predictor: An assessment that generates reports that calculates a

best fit percentage for new graduate nurses and determines the specialty units where their

clinical skills and temperament are best fit.

Best fit unit: From the Prophecy job fit predictor data, the “best fit” is considered

80% or greater.

Relevance to Nursing Practice

Nursing shortages create issues for patients, families, and hospitals. The project

site for this best fit project and local hospitals in the area are experiencing a nursing

shortage (Texas Nursing Workforce Reports, n.d.). More than 1 million nurses are

expected to retire in the next 10 to 15 years, so retaining nurses is important to help with

the nurse shortage (Ackerson, 2018). The Bureau of Labor Statistics estimates that there

will be 1 million job openings for registered nurses by 2024, making the nursing shortage

worse (American Association of Colleges of Nursing [AACN], 2018). Turnover rates for

nurses during the first 2 years of practice are higher than overall nursing retention rates.

According to national research study conducted in 2017, 25% of first year nurses and

23% of second year nurses will leave the profession impacting workplace attrition

(Nursing Solutions, 2017). New graduate nurses must transition into practice and many

12

find this adjustment stressful and difficult. This high turnover rate could be attributed to

job dissatisfaction if nurses were assigned to a specialty unit that was not a good job fit.

Advancement in nursing placement and job fit is one method that, if improved by

evidence-based nursing practices, will impact nursing care, nurse recruitment, and

nursing practice across the country for new graduate nurses (Boyd-Turner et al., 2016).

After hospital stakeholders conducted a needs assessment at the project site, it

was determined that a gap in nursing practice was present at the health care facility and

may be influencing nurse retention, thus adding to the shortage of nurses. The current

state of nursing practice in this area is obsolete, there is not a best practice guideline for

new graduate nurse placement, which led to the implementation of the Prophecy (2018)

job fit predictor quality improvement initiative at the project site in 2015. However, no

studies have been conducted up to this time to evaluate the impact of the predictor

recommendations. No strategies or standard practices have been tried to address this

issue. The goal of this best fit project was to create standard practice recommendations to

address this gap-in-practice and to determine if the Prophecy job fit predictor has an

impact on graduate nurse satisfaction. If successful, these findings can be disseminated

and possibly create practice guidelines for on boarding new graduate nurses and improve

retention rates.

Local Background and Context

The setting for this DNP best project was a large metropolitan multi hospital

system that has both acute and critical care units including medical-surgical, trauma,

intensive care, rehabilitation, pediatrics, and long-term acute care. In the state of Texas,

13

research shows a continued nursing shortage, even with legislation supporting nursing

education (Texas Nursing Workforce Reports, 2016). Local evidence of the nursing

shortage and high turnover rate state that new graduate nurses are a large percentage of

the nurses who are leaving the profession (Texas Nursing Workforce Reports, 2016).

The Texas Nursing Workforce Shortage Coalition reports that a nursing shortage will

continue without graduate nurses filling the shortage gap (Texas Nursing Workforce

Reports, 2016). The American Association of Colleges of Nursing, National Council of

State Boards of Nursing, and Institute for Healthcare Improvement are organizations that

established a relationship of graduate nurse transition in the workforce and current

policies and recommendations for new nurses. However, at this time no state or federal

guidelines address new graduate nurse unit placement they focus on orientation

techniques (Akerson & Stiles, 2018). The guiding practice-focused question for this

doctoral project was “Are satisfaction rates of recently graduated registered nurses

influenced by their unit placement being based on the Prophecy job fit predictor?” The

project site mission focuses on advancing health care by bringing together all aspects of

the health system - care delivery, physicians, and health solutions to create an evidence

based integrated health system. The project site currently utilizes the operational process

of the Prophecy (2018) job fit software to assist with the placement of new graduate

nurses in appropriate units after employment, applying evidence-based practices to

orientation and hiring processes. Prophecy job fit predictor (2018) measures specialty fit

for new graduate nurses by objectively identifying which clinical areas they will be most

successful in. Each graduate nurse receives a percentage of specialty fit for each clinical

14

area. For example, a nurse might have a 78% fit for the intensive care unit, a 57% fit for

the medical surgical unit, and a 35% fit for the emergency room. The nurse in this

example should be placed in the intensive care unit and have a high satisfaction rate. To

validate this quality improvement initiative, an evaluation of the Prophecy job fit

predictor was conducted to explain the impact of the Prophecy (2018) job fit predictor in

relation to job satisfaction. Placing nurses into the best fit practice environment along

with addressing modifiable situational factors influence nurse satisfaction and reduce

nurse turnover (Hussein, et al., 2016). Prophecy analytics provide a predictive guide that

recommends specialties that new graduate nurses would be most successful in based on

their clinical knowledge and behavioral attributes (Prophecy, 2018).

Role of DNP Student

As a future DNP-prepared nurse, I want to transform healthcare by contributing to

nursing scholarship. DNP graduates can influence healthcare by participating in

leadership roles, becoming active stakeholders, and initiating innovative system

developments by evaluating and analyzing health care improvement (Kendall, 2013). I

am currently a nurse educator in a Bachelor of Science in nursing program and want

graduate nurses to be successful after they graduate. Many new nurses are uncertain

about nursing roles, responsibilities, and in what clinical specialty they should begin their

nursing career. My role in the DNP project was to analyze data that influences the

placement of graduate nurses and creates best practice guidelines for the institution.

Walden University defines positive social change as a deliberate process of

creating and applying ideas, strategies, and actions to improve human and social

15

conditions. The best fit project can create a positive social change with this data and

reduce turnover and nursing shortage if a best practice guideline for graduate nurse

placement is established. I am passionate about nursing education and the advancement

of graduate nurse transition into the workplace. The AACN (2006) states the importance

of nursing scholarship is to translate, integrate, and disseminate information to improve

health care. As defined by the AACN (2006), nursing scholarship involves activities that

advance the practice of nursing through thorough analysis that is significant to the

profession and can be documented, replicated, or elaborated. The evaluation of practice,

quality improvement, and reliability of practice are mandated by government and

healthcare officials and educating healthcare officials on relevant and current evidence-

based information will promote patient and safety outcomes.

In today’s healthcare system, practitioners rely on evidence-based practices to

ensure that best practices are being utilized in any health care setting. This DNP project

questioned if the use of the Prophecy job fit placement predictor correctly identifies the

units that new graduate nurses will learn, thrive, and ultimately be successful in. The

positive social change will be redefining the social norm of all new graduates being

placed in any hospital unit based strictly on interviewer opinion or hospital nurse

vacancy. By establishing evidence-based practice guidelines on graduate nurse unit

placement it will provide consistency upon hiring new nurses. As a nurse educator and

graduate nurse advocate, I may have personal biases toward creating policies for graduate

nurse placement. However, I will rely on raw data, statistical analysis, and research

during this project to guide my recommendations. Proper placement should improve

16

graduate nurse satisfaction and result in improved patient outcomes and turnover and

burnout rates (Perry, 2018). A study conducted by Hussein (2016) concluded that

personal characteristics and situational factors influence new graduate nurses’ satisfaction

with the practice environment. Completing this evidence-based practice project within

the project site evaluated the quality improvement initiative that was implemented based

on a needs assessment.

Summary

The purpose of this DNP best fit project was to analyze the impact of the

Prophecy job fit predictor on graduate nursing satisfaction in their current hospital

setting. Policies have not been adapted to guide best practice and this gap in practice led

to the Prophecy quality improvement initiate. Placing graduate nurses in their best fit

specialty can contribute to improving nursing satisfaction. Nursing satisfaction has been

linked to knowledge and use of professional competencies (de Souza Moreira, 2018). The

concepts that were utilized to evaluate current practice was the PDSA method and the

Herzberg’s two factor theory. To correct this identified gap-in-practice, collection and

analysis of the results from the quality improvement initiative can provide support for

future placement practices and determine if the Prophecy job fit predictor improves

graduate nurse satisfaction. The collection and analysis of Prophecy job fit predictor data

and Casey-Fink graduate nurse survey results will be described in section 3.

17

Section 3: Collection and Analysis of Evidence

Introduction

The nursing shortage can be contributed in part to a high graduate nurse turnover

rate (National Council of State Boards of Nursing, 2018), but research has illustrated that

facilities with high nursing satisfaction have high nurse retention (Coleman, 2018). Thus,

the purpose of this best fit project was to identify whether nurse clinical unit placement

contributes to new graduate nurses’ job satisfaction. This section of the paper will include

(a) the practice focused question, (b) the sources of evidence, (c) the archival and

operational data, and the (d) analysis and syntheses for this DNP best fit project.

Practice-Focused Question

There are limited quality improvement initiatives and research around graduate

nurse placement. But the project site identified a problem with nursing shortages on

specialty units and began the quality improvement initiative using the Prophecy job fit

predictor. With no practice guidelines in place for new graduate nurse placement upon

hire, the quality improvement initiative was established to form baseline information to

create policies and guidelines for leadership when hiring new graduates. The guiding

practice-focused question for this doctoral project was “Are satisfaction rates of recently

graduated registered nurses influenced by their unit placement being based on the

Prophecy job fit predictor?”

Operational definitions include the Casey-Fink survey, which is a survey used to

provide statistics on demographic information, skills/procedure performance,

comfort/confidence, job satisfaction dimensions, and work environment and difficulties

18

in role transition; Prophecy job fit predictor, which helps determine the specialty units

where their clinical skills and temperament are best fit; and best fit unit, which is

considered 80% or greater from the Prophecy job fit predictor data.

Sources of Evidence

The sources of evidence used to address the practice question include literature

related to nurse satisfaction, retention, and Prophecy job fit gathered from a database

search of CINAHL, OVID, and MEDLINE. Key words included graduate nurse,

satisfaction, nursing shortage, retention, and turnover. Sources of evidence were also

obtained from the AACN, Vizient, National Council of State Boards of Nursing,

Prophecy, and Institute for Healthcare Improvement. Vizient and Prophecy are the

project site databases that collect and store the Casey-Fink graduate nurse survey results

and the Prophecy job fit predictor data. The AACN, National Council of State Boards of

Nursing, and Institute for Healthcare Improvement are organizations that established a

relationship of graduate nurse transition in the workforce and current policies and

recommendations for new nurses. The scope of this review was concentrated over the last

8 years.

The evidence was used to support the results of the analysis of the relationship

between Prophecy job fit data and nursing satisfaction. The project site collects and

calculates the Prophecy job fit predictor data and gives the Casey-Fink Survey to

graduate nurses once they complete an initial application. The hospital provided

deidentified data they collected during their quality improvement initiative for evaluation.

The graduate nurse names were excluded; only assigned unit and Prophecy score along

19

with satisfaction rates were disclosed. These results of the Prophecy job fit predictor data

were analyzed to compare new graduate nurse satisfaction rates of nurses who were

placed in their best fit unit. The analysis and findings will inform leadership and

stakeholders of the impact of Prophecy job fit predictor on nursing satisfaction and may

establish evidence-based policy guidelines on best practices for new graduate nurse

placement.

Archival and Operational Data

The nature of this project is an evaluation of an existing quality improvement

initiative that was completed by analyzing Prophecy job fit predictor data and the Casey-

Fink graduate nurse survey data. Walden University IRB and organizational approval

were obtained prior to evaluation of the survey and predictor results (approval no. 04-19-

19-0126051). Prophecy is a quality improvement tool that measures behavioral attributes

along with clinical competencies, which is used to determine appropriate job fit for new

nurses (Prophecy, 2018). The Casey-Fink graduate nurse survey is a tool that evaluates

nurse satisfaction rates.

The data were obtained from the project site that was originally collected from

one cohort of 54 graduate nurses placed in three hospital sites in six different specialty

units. Prophecy job fit data and Casey-Fink nursing satisfaction data were gathered

through nurse resident program participation. Data were recorded in specified intervals—

initial, 6 months, and 12 months—and are stored electronically via Vizient, and the

Prophecy website. Access is password protected and can only be authorized by approved

leadership and personnel to maintain confidentiality. Permission to access operational

20

data from Prophecy and Vizient was granted by the project site IRB and nursing

administration. The primary outcomes to be measured during the project was the impact

of the Prophecy job fit predictor on nursing satisfaction as assessed with the Casey-Fink

survey. This data supported the outcome of the project.

Analysis and Synthesis

The program used for recording, organizing, and analyzing the data obtained from

the Prophecy job fit predictor and the Casey-Fink survey evidence include SPSS (2018)

data to perform a descriptive analysis with graphs of the percentages for the three

hospitals where the cohort of graduate nurses were placed and then compare the three

figures using inferential analysis. The graph illustrates the findings from a one-way

ANOVA where a comparison of the mean Prophecy best fit predictor analysis scores of

the three hospitals will determine if there are any statistical differences. Analytic methods

were used to draw inferences from the data. If any outliers existed they were defined and

excluded from the analysis. Ethical considerations, such as the right to privacy, protection

from harm, and voluntary consent, do not apply to quality improvement projects because

the participants are not regarded as research subjects. There was no requirement for the

graduate nurses to reveal their individual identity, de-identified data was provided by the

facility. Data was collected by cohort. Each participant and cohort received an assigned

random number for correlation for the Prophecy predictor job fit placement data and

Casey-Fink graduate nurse experience survey. The correlation was based on the cohort of

graduate nurses, hospital site, what hospital unit they were placed, and Prophecy

predictor job fit placement data. A systematic analysis was conducted from the Prophecy

21

job fit data in comparison with the results of the Casey-Fink graduate nurse experience

survey. An analysis was conducted to review the percentage of the cohort that was placed

in their best fit specialty and the cohort overall nursing satisfaction rate. The data is

stored and protected by the hospital site via a quality improvement product website that is

password encrypted and can only be viewed with authorized access. An Education

Resource Specialist accessed data and provided de-identified information by creating

random assigned numbers for analysis.

Summary

The purpose of this DNP project was to evaluate an existing quality improvement

initiative by analyzing the Prophecy job fit predictor and the Casey-Fink graduate nurse

experience surveys. To analyze the Prophecy quality improvement initiative, an

evaluation of the Prophecy job fit predictor was conducted to explain the impact of the

Prophecy (2018) job fit predictor in relation to job satisfaction. The percentage of a

cohort that was placed in their best fit unit and the cohort overall nursing satisfaction rate

using the revised Casey-Fink graduate nurse experience survey was analyzed. A

systematic analysis of Prophecy job fit data in comparison with Casey-Fink survey data

was conducted. The primary outcomes to be measured during the project were the

effectiveness and impact of the Prophecy job fit predictor on nursing satisfaction as

assessed by the Casey-Fink survey. The results from the data analysis will then be used to

answer the practice-focus question. The findings and recommendations that will be

explained in Section 4, will be disseminated to nursing leaders and nursing management

within the project site.

22

Section 4: Findings and Recommendations

Introduction

The local problem that I addressed in this project was the high turnover rates

among new graduate nurses by evaluating the impact of the Prophecy (2018) job fit

predictor on nursing satisfaction rates of recently graduated (within 6 months) registered

nurses. The U.S. Bureau of Labor Statistics (2018) has projected that 1.1 million

additional nurses will be needed to avoid a continued shortage in health care. The nursing

profession continues to face shortages due to lack of potential educators, high turnover,

and inequitable distribution of the workforce (Sawaenqdee et al., 2016). The new

graduate nurse turnover rate had not been calculated at this project site; however,

according to nurse recruiters at the project site, there had been a consistent nursing

shortage when new graduate nurses were placed on specialty units. Best practices for

graduate nurse placement had not been researched, and no evidence-based practice

guidelines had been established.

This lack of research on job fit and placement left a gap-in-practice, so the

Prophecy job fit predictor was implemented to use when guiding nurse recruiters and unit

managers when hiring new graduates. The purpose of this doctoral project was to provide

data that can be used to encourage policy development for graduate nurse placement. The

sources of evidence used to address the practice question included a literature review

related to nurse satisfaction, retention, and Prophecy job fit. Literature were obtained

through a search of CINAHL, OVID, and MEDLINE. Key words included graduate

nurse, satisfaction, nursing shortage, retention, and turnover. Other organizations that

23

provided sources of evidence are the AACN, Vizient, National Council of State Boards

of Nursing, Prophecy, and Institute for Healthcare Improvement. The data obtained from

the Prophecy job fit predictor and the Casey-Fink survey provided evidence for analysis.

The program used for descriptive analysis, which involved recording, organizing, and

analyzing the evidence, was SPSS (2018).

Findings and Implications

Approximately 25% of new nurses leave a position within their first year of

practice (National Council of State Boards of Nursing, 2018). Increased turnover

influences patient safety and health care outcomes. However, the Prophecy job fit

predictor assessment data has enabled managers to make better-informed decisions about

nurse candidate placement and success. The goal of this DNP project was to identify if

job placement contributed to new graduate nurses’ satisfaction. This DNP project may

impact the field of nursing by providing managers and nurse recruiters guidelines for

counseling new graduates after hiring and provide direction when placing new graduate

nurses in specialty units. To answer the question of whether the Prophecy job fit predictor

improved graduate nurse satisfaction in specialty units, I measured nurse satisfaction and

will describe the impact of the Prophecy job fit indicator.

Data Analysis

The guiding practice-focused question for this doctoral project was “Are nursing

satisfaction rates of recently graduated (within 6 months) registered nurses influenced

by their placement being based on the Prophecy job fit predictor?” There were 54 nurse

graduate participants in the cohort at three different hospital locations. The

24

measurement of nursing satisfaction rates for new graduate nurses was done on a Likert

scale to evaluate the Casey-Fink Graduate nurse survey results. The mean rating of the

Casey-Fink graduate nurse survey is presented at initial, 6 months and 12 months for

each hospital location where the nurse graduate participants were placed (Figures 1-6).

Figure 1. Specialty unit satisfaction for Hospital A.

Figure 2. Overall unit satisfaction for Hospital A. Overall unit satisfaction, 3.25 below

benchmark of 3.26.

25

Figure 3. Specialty unit satisfaction for Hospital B.

Figure 4. Overall unit satisfaction for Hospital B. Overall unit satisfaction, 2.5 below

benchmark of 3.26.

26

Figure 5. Specialty unit satisfaction for Hospital C.

Figure 6. Overall unit satisfaction for Hospital C. Overall unit satisfaction, 3.08 below

benchmark of 3.26.

From the Prophecy job fit predictor data, the best fit is considered 80% or

greater. For Hospital A, 58.33% (14 out of 24) graduate nurse residents were placed in

their best fit specialty (see Figure 7). Two nurse residents were excluded because no

prophecy predictor assessment is available for their specialty unit. This unanticipated

limitation did not have an impact on the findings. Hospital A’s best fit placement

percentage had a 12 month mean satisfaction score of 3.34. At Hospital B, 20.00% (2

out of 10) graduate nurse residents were placed in their best fit specialty (see Figure 7).

Hospital B’s best fit placement percentage had a 12 month mean satisfaction score of

3.1. Finally, at Hospital C, 33.33% (6 out of 18) graduate nurse residents were placed in

their best fit specialty (see Figure 7). Hospital C’s best fit placement percentage had a

12 month mean satisfaction score of 3.1. Overall, results showed that a correlation

between best fit and mean satisfaction score was established.

27

Figure 7. Bar graph illustrating the 80 percent and above best fit participants at each

hospital.

The best fit placement analysis scores were in ratio form and the participants

formed three independent groups at the three hospital locations. The best inferential test

to examine any statistically significant differences among the best fit placement analysis

scores is a one-way ANOVA (Sheskin, 2011). The ANOVA was run, and a statistically

significant omnibus test was found at F (2, 50) = 3.328, p = 0.044 (see Table 1). In Table

2, Tukey post hoc tests revealed there were significant mean differences between

Hospital A (82.30) and Hospital B (68.60). There were no other significant mean

differences between hospitals (see Figure 8).

28

Table 1

Tests of Between-Subjects Effects

Source Type III df Mean

Square

F p

Sum of squares

Hospital 1528.799 2 764.400 3.328 .044

Error 11484.220 50 229.684

Total 323718.000 53

Table 2

Tukey Post Hoc Test Among Means

Hospital N Subset

1 2

Hospital B 10 68.600

Hospital C 20 73.9500 73.9500

Hospital A 23 82.3043

29

Figure 8. Mean plot for three means among the hospitals.

The outcome from the project demonstrated that the placement of graduate

nurses utilizing the Prophecy job fit predictor can influence satisfaction rates which can

provide newly hired graduates specific information for their best fit specialty. The

outcome from the project also demonstrated that the utilization of the Prophecy job fit

predictor can provide guidance and can increase knowledge about nurse satisfaction

and its relevance to their clinical practice unit. Based on the outcome of the DNP

project, the evidence-based information gathered can benefit the individual graduate

nurse as they transition into the workforce by providing direction and guidance. The

impact of the Prophecy job fit predictor can also be shared within the community

hospital, to gain more insight about job placement and nursing satisfaction in the

community setting. The potential implication for the use of the Prophecy job fit

predictor being used for all new graduate nurses will allow modification in practices

30

and policies, allowing the health care system to take a more active role in nurse

placement and retention strategies. The implications from this doctoral project study in

regard to social change is that it has the potential to improve nursing practice by

establishing baseline data for best practice guidelines for new graduate nurses.

Recommendations

Best practice guidelines have not been established for placement of new graduate

nurses upon hiring. The proposed solution that will address this gap-in-practice is

creating a policy including the best practice guidelines that will put into effect the use of

the Prophecy job fit predictor when assigning graduate nurses to their hospital setting.

This DNP project was able to support the use of the Prophecy job fit predictor. The

outcome information will also provide valuable information for developing future

programs to improve graduate nurse satisfaction. Demands on our healthcare system

make it imperative for nurses to contribute to evidence based practices to improve health

care and reduce the nursing shortage.

Strengths and Limitations of the Project

The goal of this quality improvement project was to evaluate the impact of the

Prophecy job fit predictor on graduate nurse satisfaction. One strength of this project is

that the Prophecy job fit predictor and Casey-Fink graduate nurse survey were already

being implemented at the project site and many hiring nurses were currently utilizing the

data that it provides. The DNP nurse leader can create new guidelines and improve the

graduate nurse experience. According to Albanese et al. (2010), “Data become more

relevant when nurses realize that improvements in one quality indicator results in

31

subsequent positive changes in another” (p. 240). By providing statistical evidence of the

impact of the Prophecy job fit predictor it supports the quality improvement initiative.

The data obtained from this evaluation project enables the stakeholders to make an

informed decision when defining best practices and modifying policies.

A limitation of the project is the small size, only one cohort of 54 graduate nurses

was analyzed during this project. Therefore, the outcomes of this DNP project cannot be

generalized. Another limitation of the project is that it is not a research study and a true

cause and effect relationship cannot be assumed or established. Future recommendations

include conducting a longitudinal research study to allow more time to focus on the

impact of the Prophecy job fit predictor and establish a more definitive cause and effect

relationship.

32

Section 5: Dissemination Plan

After my DNP project analysis is shared with stakeholders and experts, the final

draft will be developed into a poster presentation. I will be presenting the analysis of the

Prophecy job fit predictor and Casey-Fink graduate nurse satisfaction survey by using a

poster presentations because they have been shown to increase knowledge and

confidence for nurses (Ilic & Rowe, 2013). The poster presentation will be available for

nurse managers, nurse recruiters, and leadership within the organization. When

presenting information to various specialties and levels of nurses, poster presentations are

easy to understand by providing charts and diagrams that help to explain difficult

statistical data and concepts. I plan to display my poster at a leadership meeting at the

project site, which will be convenient for nurses to approach the poster and look it at their

leisure, determine whether they want to know more, and ask questions (Rowe & Ilic,

2015).

Sharing my research will contribute to evidence-based research. Part of the

responsibility of a scholar–practitioner is to contribute to the science of nursing and

become a leader in the profession. Change is defined as the transformation of tasks,

processes, methods, structures, and relationships that are necessary for organizational

survival (White, Dudley-Brown, & Terhaar, 2016). Nurse leaders have the responsibility

to ensure that positive change is occurring at the organization and this can occur with

disseminating my DNP project. Future dissemination at local and national conferences

will allow many nurses to review the analysis and findings from this DNP project.

33

Analysis of Self

The importance of nursing scholarship is to translate, integrate, and disseminate

information to improve health care (AACN, 2006). The evaluation of practice, quality

improvement, and reliability of practice are mandated by government and healthcare

officials educating them on relevant and current evidence-based information promoting

patient and safety outcomes. The dissemination of quality initiatives and projects

provides a means of sharing information so that fellow health care professionals, health

administrators, and policy makers can learn from each other. They can use the gained

knowledge to develop or adapt research that will contribute to advancing evidence-based

practice to improve nurse residency programs, orientation, and hiring practices. An

example of scholarship that demonstrates the role of the DNP-prepared nurse is

translating evidence into practice.

As a nursing leader, I can contribute to making a difference by incorporating

evidence-based practice into organizational policies to ensure that best practices are being

used in all departments of the health care system. Further, nursing scholarship involves

activities that can advance nursing practice through significant research that can inspire

further research (AACN, 2006). As a scholar–practitioner, I used evidence-based practice

to advance nursing practice and translate evidence into practice to improve patient and

health outcomes (Zaccagnini & White, 2014). The results of this quality improvement

project provide information that will guide policy making and best practice guidelines.

The benefits of practice guidelines include developing an evidence-based consensus of

the best practices, consolidating sources of information regarding outcomes, and

34

preparing information into a usable format for practitioners (Cross, Hardee, Jewell,

2001).

Summary

To conclude, the focus of this DNP project was the analysis of a quality

improvement initiative to evaluate the impact of the Prophecy job fit predictor and

graduate nurse satisfaction based on the Casey-Fink nurse survey. The nursing shortage is

continuing to impact healthcare. According to the Bureau of Labor Statistics’

Employment Projections 2014-2024, the registered nurse workforce is expected to grow

from 2.7 million in 2014 to 3.2 million in 2024, an increase of 439,300 or 16%. The

project brought an opportunity for the organization to evaluate the impact of the

Prophecy job fit predictor and guide change for this growing workforce that can end

shortages. The project involved reviewing the data from one cohort of graduate nurses

that was placed in three hospitals at the project site. The results showed that there was a

relationship between Prophecy job fit and nursing satisfaction. The hospital that placed

the highest percentage of its graduate nurses in their best fit specialty had the highest

satisfaction scores.

The results of this doctoral project demonstrated that the Prophecy job fit

predictor during nursing orientation can guide nurses into the appropriate specialty unit

and increase nursing satisfaction. The objectives of the DNP project were the

measurement of nursing satisfaction rates of new graduate nurses and explanation of the

impact of the Prophecy job fit predictor using descriptive analysis of the percentages. The

dissemination plan for this doctoral project includes a 30-minute poster presentation to

35

various stakeholders sharing the results of the project. The social impact of this DNP

project can influence the nursing shortage by using evidence-based practice in new nurse

graduate placement to improve nursing satisfaction resulting in decreased turnover and

burnout rates.

The data collected from this quality improvement project evaluation offers

information related to developing best practice guidelines. It is recommended that further

quality improvement initiatives and research related to Prophecy job fit predictor and

Casey-Fink graduate nurse satisfaction survey be conducted. Continuing to apply

evidence-based information to policies and best practice guidelines aligns with the goals

and missions of the organization and contributes to nursing excellence.

36

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